Shannon Quinn

P2Y6 Receptor Antagonist MRS2578 Inhibits Neutrophil Activation and Aggregated Neutrophil Extracellular Trap Formation Induced by Gout-Associated Monosodium Urate Crystals

Human neutrophils (polymorphonuclear leukocytes [PMNs]) generate inflammatory responses within the joints of gout patients upon encountering monosodium urate (MSU) crystals. Neutrophil extracellular traps (NETs) are found abundantly in the synovial fluid of gout patients. The detailed mechanism of MSU crystal–induced NET formation remains unknown. Our goal was to shed light on possible roles of purinergic signaling and neutrophil migration in mediating NET formation induced by MSU crystals. Interaction of human neutrophils with MSU crystals was evaluated by high-throughput live imaging using confocal microscopy. We quantitated NET levels in gout synovial fluid supernatants and detected enzymatically active neutrophil primary granule enzymes, myeloperoxidase, and human neutrophil elastase. Suramin and PPADS, general P2Y receptor blockers, and MRS2578, an inhibitor of the purinergic P2Y6 receptor, blocked NET formation triggered by MSU crystals. AR-C25118925XX (P2Y2 antagonist) did not inhibit MSU crystal–stimulated NET release. Live imaging of PMNs showed that MRS2578 represses neutrophil migration and blocked characteristic formation of MSU crystal–NET aggregates called aggregated NETs. Interestingly, the store-operated calcium entry channel inhibitor (SK&F96365) also reduced MSU crystal–induced NET release. Our results indicate that the P2Y6/store-operated calcium entry/IL-8 axis is involved in MSU crystal–induced aggregated NET formation, but MRS2578 could have additional effects affecting PMN migration. The work presented in the present study could lead to a better understanding of gouty joint inflammation and help improve the treatment and care of gout patients.

Scalable Fast Rank-1 Dictionary Learning for fMRI Big Data Analysis

It has been shown from various functional neuroimaging studies that sparsity-regularized dictionary learning could achieve superior performance in decomposing comprehensive and neuroscientifically meaningful functional networks from massive fMRI signals. However, the computational cost for solving the dictionary learning problem has been known to be very demanding, especially when dealing with large-scale data sets. Thus in this work, we propose a novel distributed rank-1 dictionary learning (D-r1DL) model and apply it for fMRI big data analysis. The model estimates one rank-1 basis vector with sparsity constraint on its loading coefficient from the input data at each learning step through alternating least squares updates. By iteratively learning the rank-1 basis and deflating the input data at each step, the model is then capable of decomposing the whole set of functional networks. We implement and parallelize the rank-1 dictionary learning algorithm using Spark engine and deployed the resilient distributed dataset (RDDs) abstracts for the data distribution and operations. Experimental results from applying the model on the Human Connectome Project (HCP) data show that the proposed D-r1DL model is efficient and scalable towards fMRI big data analytics, thus enabling data-driven neuroscientific discovery from massive fMRI big data in the future.

Template-guided Functional Network Identification via Supervised Dictionary Learning

Functional network analysis based on matrix decomposition/factorization methods including ICA and dictionary learning models have become a popular approach in fMRI study. Yet it is still a challenging issue in interpreting the result networks because of the inter-subject variability and image noises, thus in many cases, manual inspection on the obtained networks is needed. Aiming to provide a fast and reliable functional network identification tool for both normal and diseased brain fMRI data analysis, in this work, we propose a novel supervised dictionary learning model based on rank-1 matrix decomposition algorithm (S-r1DL) with sparseness constraint. Application on the Autism Brain Imaging Data Exchange (ABIDE) database showed that S-r1DL can fast and accurately identify the functional networks based on the given templates, comparing to unsupervised learning method.

Distributed rank-1 dictionary learning: Towards fast and scalable solutions for fMRI big data analytics

The use of functional brain imaging for research and diagnosis has benefitted greatly from the recent advancements in neuroimaging technologies, as well as the explosive growth in size and availability of fMRI data. While it has been shown in literature that using multiple and large scale fMRI datasets can improve reproducibility and lead to new discoveries, the computational and informatics systems supporting the analysis and visualization of such fMRI big data are extremely limited and largely under-discussed. We propose to address these shortcomings in this work, based on previous success in using dictionary learning method for functional network decomposition studies on fMRI data. We presented a distributed dictionary learning framework based on rank-1 matrix decomposition with sparseness constraint (D-r1DL framework). The framework was implemented using the Spark distributed computing engine and deployed on three different processing units: an in-house server, in-house high performance clusters, and the Amazon Elastic Compute Cloud (EC2) service. The whole analysis pipeline was integrated with our neuroinformatics system for data management, user input/output, and real-time visualization. Performance and accuracy of D-r1DL on both individual and group-wise fMRI Human Connectome Project (HCP) dataset shows that the proposed framework is highly scalable. The resulting group-wise functional network decompositions are highly accurate, and the fast processing time confirm this claim. In addition, D-r1DL can provide real-time user feedback and results visualization which are vital for large-scale data analysis.

Implementing dictionary learning in Apache Flink, Or: How I learned to relax and love iterations

The authors evaluate the use of Apache Flink, a novel data analysis framework offering optimizations over competitors such as Apache Spark, in order to use a rank-1 dictionary learning (r1DL) algorithm to decompose fMRI data. We first expand the functionality of the Flink Python API in order to accommodate the implementation of rank-1 dictionary learning, a model for decomposing a large matrix. Iterative algorithms, aggregators, and other features are added to the incomplete Python API, and the experiences and lessons learned are described. Using these features, we port an existing implementation of r1DL from using the Python API of Apache Spark to using the Python API of Apache Flink. In preliminary testing, this implementation suggests performance boosts over Spark for large input files, meriting further research. We conclude that Flink is likely a feasible tool for the application of dictionary learning to decompose fMRI data, and we continue to evaluate and apply it.

Mining Pre-Exposure Prophylaxis Trends in Social Media

Pre-Exposure Prophylaxis (PrEP) is a ground-breaking biomedical approach to curbing the transmission of Human Immunodeficiency Virus (HIV). Truvada, the most common form of PrEP, is a combination of tenofovir and emtricitabine and is a once-daily oral mediation taken by HIV-seronegative persons at elevated risk for HIV infection. When taken reliably every day, PrEP can reduce ones risk for HIV infection by as much as 99%. While highly efficacious, PrEP is expensive, somewhat stigmatized, and many health care providers remain uninformed about its benefits. Data mining of social media can monitor the spread of HIV in the United States, but no study has investigated PrEP use and sentiment via social media. This paper describes a data mining and machine learning strategy using natural language processing (NLP) that monitors Twitter social media data to identify PrEP discussion trends. Results showed that we can identify PrEP and HIV discussion dynamics over time, and assign PrEP-related tweets positive or negative sentiment. Results can enable public health professionals to monitor PrEP discussion trends and identify strategies to improve HIV prevention via PrEP.